This study proposes an improved fuzzy time series (IFTS) forecasting model using variations of data that can interpolate historical data and forecast the future. The parameters in this model are chosen by algorithms to obtain the most suitable values for each data set. The calculation of the IFTS model can be performed conveniently and efficiently by a procedure within the R statistical software that has been stored in the AnalyseTS package. The proposed model is also used in the forecasting of two real problems in Vietnam: the penetration of salt and the total population. These numerical examples show the advantages of the proposed model in comparison with existing models and illustrate its effectiveness in practical applications.

中文翻译：

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基于数据变化的改进的模糊时间序列预测模型

这项研究提出了一种改进的模糊时间序列（IFTS）预测模型，该模型使用可以插值历史数据并预测未来的数据变化。该模型中的参数由算法选择，以获得每个数据集最合适的值。IFTS模型的计算可以通过已存储在AnalyseTS软件包中的R统计软件内的过程方便而有效地执行。所提出的模型还用于预测越南的两个实际问题：盐的渗透率和总人口。这些数值示例显示了该模型与现有模型相比的优势，并说明了其在实际应用中的有效性。